diff --git a/DESCRIPTION b/DESCRIPTION
index 16e4363..74e2694 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -62,4 +62,4 @@ Suggests:
 Remotes:
     ohdsi/ReportGenerator,
     ohdsi/ResultModelManager
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
diff --git a/R/estimation-cohort-method-covariateBalance.R b/R/estimation-cohort-method-covariateBalance.R
index 7041056..39656b2 100644
--- a/R/estimation-cohort-method-covariateBalance.R
+++ b/R/estimation-cohort-method-covariateBalance.R
@@ -112,6 +112,7 @@ cohortMethodCovariateBalanceServer <- function(
           analysisId = row$analysisId)},
           error = function(e){return(data.frame())}
         )
+        
         return(balance)
       })
       
@@ -127,6 +128,22 @@ cohortMethodCovariateBalanceServer <- function(
         )
       
       balancePlot <- shiny::reactive({
+        
+        row <- selectedRow()
+        if(is.null(row$targetId)){
+          return(NULL)
+        }
+        
+        maxSdmStatistic <- estimationGetMaxSdm(
+          connectionHandler = connectionHandler,
+          resultDatabaseSettings = resultDatabaseSettings,
+          targetId = row$targetId,
+          comparatorId = row$comparatorId,
+          outcomeId = row$outcomeId,
+          analysisId = row$analysisId,
+          databaseId = row$databaseId
+        ) 
+        
         if (is.null(balance()) || nrow(balance()) == 0) {
           return(NULL)
         } else {
@@ -134,7 +151,8 @@ cohortMethodCovariateBalanceServer <- function(
             balance = balance(),
             beforeLabel = "Before propensity score adjustment",
             afterLabel = "After propensity score adjustment",
-            textsearch = textSearchCohortMethod
+            textsearch = textSearchCohortMethod,
+            maxSdmStatistic
           )
           return(plot)
         }
@@ -151,7 +169,7 @@ cohortMethodCovariateBalanceServer <- function(
           row <- selectedRow()
           text <- "<strong>Figure 3.</strong> Covariate balance before and after propensity score adjustment. Each dot represents
       the standardizes difference of means for a single covariate before and after propensity score adjustment on the propensity
-      score. Move the mouse arrow over a dot for more details."
+      score. The maximum absolute standardized difference of the mean (Max SDM) is given at the top of the figure. Move the mouse arrow over a dot for more details."
           return(shiny::HTML(sprintf(text)))
         }
       })
@@ -421,13 +439,24 @@ getCohortMethodCovariateBalanceSummary <- function(
   
 }
 
+cmDiagnostics <- shiny::reactive({
+  estimationGetCmDiagnostics(
+    connectionHandler = connectionHandler,
+    resultDatabaseSettings = resultDatabaseSettings,
+    targetIds =  targetIds,
+    comparatorIds = comparatorIds,
+    outcomeId = outcomeId
+  )
+})
+
 
 
 plotCohortMethodCovariateBalanceScatterPlotNew <- function(
     balance,
     beforeLabel = "Before propensity score adjustment",
     afterLabel = "After propensity score adjustment",
-    textsearch = shiny::reactiveVal(NULL)
+    textsearch = shiny::reactiveVal(NULL),
+    maxSdmStatistic = NULL
 ){
   
   if(is.null(textsearch())){
@@ -469,6 +498,7 @@ plotCohortMethodCovariateBalanceScatterPlotNew <- function(
   ) %>%
     plotly::layout(
       #shapes = list(xyline(limits)),
+      title = ~paste0("Max SDM Statistic = ", maxSdmStatistic),
       shapes = list(list(
         type = "line", 
         x0 = 0, 
@@ -589,6 +619,57 @@ getCmOptions <- function(connectionHandler,
   
 }
 
+estimationGetMaxSdm <- function(
+    connectionHandler = connectionHandler,
+    resultDatabaseSettings = resultDatabaseSettings,
+    targetId =  targetId,
+    comparatorId = comparatorId,
+    outcomeId = outcomeId,
+    analysisId = analysisId,
+    databaseId = databaseId
+){
+  
+  sql <- "
+    SELECT DISTINCT
+      dmd.cdm_source_abbreviation database_name,
+      cmds.analysis_id,
+      cmds.target_id,
+      cmds.comparator_id,
+      cmds.outcome_id,
+      cmds.max_sdm,
+      cmds.ease
+    FROM
+      @schema.@cm_table_prefixdiagnostics_summary cmds
+      INNER JOIN @schema.@database_table dmd ON dmd.database_id = cmds.database_id
+      
+      where cmds.target_id = @target_id
+      and cmds.comparator_id = @comparator_id
+      and cmds.outcome_id = @outcome_id
+      and cmds.analysis_id = @analysis_id
+      and cmds.database_id = '@database_id'
+      ;
+  "
+  
+  result <- connectionHandler$queryDb(
+    sql = sql,
+    schema = resultDatabaseSettings$schema,
+    cm_table_prefix = resultDatabaseSettings$cmTablePrefix,
+    database_table = resultDatabaseSettings$databaseTable,
+    target_id = targetId,
+    comparator_id = comparatorId,
+    outcome_id = outcomeId,
+    analysis_id = analysisId,
+    database_id = databaseId
+  )
+  
+  maxSdm <- round(result$maxSdm, 4)
+  
+  return(
+    maxSdm
+  )
+  
+}
+
 
 
 
diff --git a/R/estimation-cohort-method-power.R b/R/estimation-cohort-method-power.R
index 2acec47..d54cb99 100644
--- a/R/estimation-cohort-method-power.R
+++ b/R/estimation-cohort-method-power.R
@@ -30,10 +30,25 @@ cohortMethodPowerViewer <- function(id) {
   ns <- shiny::NS(id)
   
   shiny::div(
-    shiny::uiOutput(outputId = ns("powerTableCaption")),
-    shiny::tableOutput(outputId = ns("powerTable")),
-    shiny::uiOutput(outputId = ns("timeAtRiskTableCaption")),
-    shiny::tableOutput(outputId = ns("timeAtRiskTable"))
+    shiny::tabsetPanel(
+      type = 'pills',
+      id = ns('power'),
+      
+      shiny::tabPanel(
+        title = "Power Table",
+        resultTableViewer(ns("powerTable"),
+                          downloadedFileName = "powerTable-"),
+        shiny::uiOutput(outputId = ns("powerTableCaption"))
+      ),
+      
+      shiny::tabPanel(
+        title = "TAR Table",
+        resultTableViewer(ns("timeAtRiskTable"),
+                          downloadedFileName = "timeAtRiskTable-"),
+        shiny::uiOutput(outputId = ns("timeAtRiskTableCaption"))
+      )
+      
+    )
   )
 }
 
@@ -74,36 +89,102 @@ cohortMethodPowerServer <- function(
         }
       })
       
-      output$powerTable <- shiny::renderTable({
+      powerTable <- shiny::reactive({
         row <- selectedRow()
         if (is.null(row$target)) {
           return(NULL)
         } else {
-            table <- prepareCohortMethodPowerTable(
-              row, 
-              connectionHandler = connectionHandler, 
-              resultDatabaseSettings = resultDatabaseSettings
-              )
-            if (!row$unblind) {
-              table$targetOutcomes  <- NA
-              table$comparatorOutcomes   <- NA
-              table$targetIr   <- NA
-              table$comparatorIr   <- NA
-            }
-            colnames(table) <- c("Target subjects",
-                                 "Comparator subjects",
-                                 "Target years",
-                                 "Comparator years",
-                                 "Target events",
-                                 "Comparator events",
-                                 "Target IR (per 1,000 PY)",
-                                 "Comparator IR (per 1,000 PY)",
-                                 "MDRR")
+          table <- prepareCohortMethodPowerTable(
+            row, 
+            connectionHandler = connectionHandler, 
+            resultDatabaseSettings = resultDatabaseSettings
+          )
+          if (!row$unblind) {
+            table$targetOutcomes  <- NA
+            table$comparatorOutcomes   <- NA
+            table$targetIr   <- NA
+            table$comparatorIr   <- NA
+          }
+          colnames(table) <- c("targetSubjects",
+                               "comparatorSubjects",
+                               "targetYears",
+                               "comparatorYears",
+                               "targetEvents",
+                               "comparatorEvents",
+                               "targetIr", # (per 1,000 PY)",
+                               "comparatorIr", # (per 1,000 PY)",
+                               "mdrr")
           
           return(table)
         }
       })
       
+      estimationPowerTableColDefs <- function(){
+        result <- list(
+          targetSubjects = reactable::colDef(
+            header = withTooltip("Target Subjects",
+                                 "Number of subjects in the target cohort"),
+            filterable = T
+          ),
+          comparatorSubjects = reactable::colDef(
+            header = withTooltip("Comparator Subjects",
+                                 "Number of subjects in the comparator cohort"),
+            filterable = T
+          ),
+          targetYears = reactable::colDef(
+            header = withTooltip("Target Years",
+                                 "Number of years of follow-up time in the target cohort"),
+            filterable = T
+          ), 
+          comparatorYears = reactable::colDef(
+            header = withTooltip("Comparator Years",
+                                 "Number of years of follow-up time in the comparator cohort"),
+            filterable = T
+          ),
+          targetEvents = reactable::colDef(
+            header = withTooltip("Target Events",
+                                 "Distinct number of outcome events in the target cohort"),
+            filterable = T
+            # cell = function(value) {
+            #   # Add < if cencored
+            #   if (value < 0 ) paste("<", abs(value)) else abs(value)
+            # }
+          ),
+          comparatorEvents = reactable::colDef(
+            header = withTooltip("Comparator Events",
+                                 "Distinct number of outcome events in the comparator cohort"),
+            filterable = T
+            # cell = function(value) {
+            #   # Add < if cencored
+            #   if (value < 0 ) paste("<", abs(value)) else abs(value)
+            # }
+          ),
+          targetIr = reactable::colDef(
+            header = withTooltip("Target IR (per 1,000 PY)",
+                                 "Incidence rate per 1,000 person-years in the target cohort"),
+            filterable = T
+          ),
+          comparatorIr = reactable::colDef(
+            header = withTooltip("Comparator IR (per 1,000 PY)",
+                                 "Incidence rate per 1,000 person-years in the comparator cohort"),
+            filterable = T
+          ),
+          mdrr = reactable::colDef(
+            header = withTooltip("MDRR",
+                                 "The minimum detectable relative risk"),
+            filterable = T
+          )
+        )
+        return(result)
+      } 
+      
+      resultTableServer(
+        id = "powerTable",
+        df = powerTable,
+        colDefsInput = estimationPowerTableColDefs(),
+        downloadedFileName = "powerTable-"
+      )
+      
       output$timeAtRiskTableCaption <- shiny::renderUI({
         row <- selectedRow()
         if (!is.null(row$target)) {
@@ -116,25 +197,110 @@ cohortMethodPowerServer <- function(
         }
       })
       
-      output$timeAtRiskTable <- shiny::renderTable({
+      timeAtRiskTable <- shiny::reactive({
         row <- selectedRow()
         if (is.null(row$target)) {
           return(NULL)
         } else {
-            followUpDist <- getCmFollowUpDist(
-              connectionHandler = connectionHandler,
-              resultDatabaseSettings = resultDatabaseSettings,
-              targetId = row$targetId,
-              comparatorId = row$comparatorId,
-              outcomeId = row$outcomeId,
-              databaseId = row$databaseId,
-              analysisId = row$analysisId
-            )
+          followUpDist <- getCmFollowUpDist(
+            connectionHandler = connectionHandler,
+            resultDatabaseSettings = resultDatabaseSettings,
+            targetId = row$targetId,
+            comparatorId = row$comparatorId,
+            outcomeId = row$outcomeId,
+            databaseId = row$databaseId,
+            analysisId = row$analysisId
+          )
           
           table <- prepareCohortMethodFollowUpDistTable(followUpDist)
           return(table)
         }
       })
+      
+      estimationTimeAtRiskTableColDefs <- function(){
+        result <- list(
+          Cohort = reactable::colDef(
+            header = withTooltip("Cohort",
+                                 "Indicates which cohort (target or comparator)"),
+            filterable = T
+          ),
+          Min = reactable::colDef(
+            header = withTooltip("Min",
+                                 "Minimum time (days) at-risk"),
+            filterable = T
+          ),
+          P10 = reactable::colDef(
+            header = withTooltip("P10",
+                                 "10th percentile time (days) at-risk"),
+            filterable = T
+          ), 
+          P25 = reactable::colDef(
+            header = withTooltip("P25",
+                                 "25th percentile time (days) at-risk"),
+            filterable = T
+          ),
+          Median = reactable::colDef(
+            header = withTooltip("Median",
+                                 "Median time (days) at-risk"),
+            filterable = T
+          ),
+          P75 = reactable::colDef(
+            header = withTooltip("P75",
+                                 "75th percentile time (days) at-risk"),
+            filterable = T
+          ),
+          P90 = reactable::colDef(
+            header = withTooltip("P90",
+                                 "90th percentile time (days) at-risk"),
+            filterable = T
+          ),
+          Max = reactable::colDef(
+            header = withTooltip("Max",
+                                 "Maximum time (days) at-risk"),
+            filterable = T
+          )
+        )
+        return(result)
+      } 
+      
+      resultTableServer(
+        id = "timeAtRiskTable",
+        df = timeAtRiskTable,
+        colDefsInput = estimationTimeAtRiskTableColDefs(),
+        downloadedFileName = "timeAtRiskTable-"
+      )
+      
+      output$timeAtRiskTableCaption <- shiny::renderUI({
+        row <- selectedRow()
+        if (!is.null(row$target)) {
+          text <- "<strong>Table 1b.</strong> Time (days) at risk distribution expressed as
+      minimum (min), 25th percentile (P25), median, 75th percentile (P75), and maximum (max) in the target
+     (<em>%s</em>) and comparator (<em>%s</em>) cohort after propensity score adjustment."
+          return(shiny::HTML(sprintf(text, row$target, row$comparator)))
+        } else {
+          return(NULL)
+        }
+      })
+      
+      # output$timeAtRiskTable <- shiny::renderTable({
+      #   row <- selectedRow()
+      #   if (is.null(row$target)) {
+      #     return(NULL)
+      #   } else {
+      #       followUpDist <- getCmFollowUpDist(
+      #         connectionHandler = connectionHandler,
+      #         resultDatabaseSettings = resultDatabaseSettings,
+      #         targetId = row$targetId,
+      #         comparatorId = row$comparatorId,
+      #         outcomeId = row$outcomeId,
+      #         databaseId = row$databaseId,
+      #         analysisId = row$analysisId
+      #       )
+      #     
+      #     table <- prepareCohortMethodFollowUpDistTable(followUpDist)
+      #     return(table)
+      #   }
+      # })
     })
 }
 
@@ -280,4 +446,4 @@ getCmFollowUpDist <- function(
       database_id = databaseId
     )
   )
-}
+}
\ No newline at end of file
diff --git a/R/estimation-cohort-method-propensityModel.R b/R/estimation-cohort-method-propensityModel.R
index 9cc201b..070bf1c 100644
--- a/R/estimation-cohort-method-propensityModel.R
+++ b/R/estimation-cohort-method-propensityModel.R
@@ -66,9 +66,22 @@ cohortMethodPropensityModelServer <- function(
           comparatorId = selectedRow()$comparatorId,
           databaseId = selectedRow()$databaseId,
           analysisId = selectedRow()$analysisId
-        )
+        ) %>%
+          dplyr::mutate(absBeta = abs(.data$coefficient))
       })
       
+      # ColorBrewer-inspired 3-color scale
+      Yellows <- function(x) grDevices::rgb(grDevices::colorRamp(c("#FFFFDD", "#FFFFB9", "#FFFF79"))
+                                           (x), maxColorValue = 255)
+      
+      # ColorBrewer-inspired 3-color scale
+      Blues <- function(x) grDevices::rgb(grDevices::colorRamp(c("aliceblue", "lightblue1", "skyblue2"))
+                                           (x), maxColorValue = 255)
+      
+      # ColorBrewer-inspired 3-color scale
+      Greens <- function(x) grDevices::rgb(grDevices::colorRamp(c("#E8FDCF", "yellowgreen"))
+                                          (x), maxColorValue = 255)
+      
       resultTableServer(
         id = 'propensityModelTable',
         df = data, 
@@ -77,11 +90,34 @@ cohortMethodPropensityModelServer <- function(
             show = F
             ),
           coefficient = reactable::colDef(
-            name = 'Beta', 
+            name = 'Beta',
+            cell = function(value) { 
+              if (value >= 0) paste0("+", round(value, 3)) else round(value, 3)
+              },
+            style = function(value) {
+              color <- if (value > 0) {
+                "#B0D5FE"
+              } else if (value < 0) {
+                "#FEBABA"
+              }
+              list(background = color)
+            },
             format = reactable::colFormat(
               digits = 3
             )
           ), 
+          absBeta = reactable::colDef(
+            name = 'Beta (Absolute Value)', 
+            format = reactable::colFormat(
+              digits = 3
+            ),
+            style = function(value) {
+              if (!is.numeric(value)) return()
+              normalized <- (value - min(data()$absBeta)) / (max(data()$absBeta) - min(data()$absBeta))
+              color <- Greens(normalized)
+              list(background = color)
+            }
+          ), 
           covariateName = reactable::colDef(
             name = 'Covariate'
           )
diff --git a/R/estimation-cohort-method-propensityScoreDistribution.R b/R/estimation-cohort-method-propensityScoreDistribution.R
index 7a5eb20..cbac7f1 100644
--- a/R/estimation-cohort-method-propensityScoreDistribution.R
+++ b/R/estimation-cohort-method-propensityScoreDistribution.R
@@ -32,7 +32,8 @@ cohortMethodPropensityScoreDistViewer <- function(id) {
     shiny::plotOutput(outputId = ns("psDistPlot")),
     shiny::div(shiny::strong("Figure 2."),"Preference score distribution. The preference score is a transformation of the propensity score
                                                                                                          that adjusts for differences in the sizes of the two treatment groups. A higher overlap indicates subjects in the
-                                                                                                         two groups were more similar in terms of their predicted probability of receiving one treatment over the other."),
+                                                                                                         two groups were more similar in terms of their predicted probability of receiving one treatment over the other. 
+                                                                                                         The equipoise statistic is also given at the top of the figure."),
     shiny::div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                shiny::downloadButton(outputId = ns("downloadPsDistPlotPng"),
                        label = "Download plot as PNG"),
@@ -88,7 +89,17 @@ cohortMethodPropensityScoreDistServer <- function(
             
           comparatorName <- row$comparator
           
-          plot <- plotCohortMethodPs(ps, targetName, comparatorName)
+          equipoiseStatistic <- getCohortMethodEquipoise(
+            connectionHandler = connectionHandler,
+            resultDatabaseSettings = resultDatabaseSettings,
+            targetId = row$targetId,
+            comparatorId = row$comparatorId,
+            outcomeId = row$outcomeId,
+            analysisId = row$analysisId,
+            databaseId = row$databaseId
+          ) 
+          
+          plot <- plotCohortMethodPs(ps, targetName, comparatorName, equipoiseStatistic)
           return(plot)
         }
       })
@@ -113,6 +124,55 @@ cohortMethodPropensityScoreDistServer <- function(
   )
 }
 
+getCohortMethodEquipoise <- function(
+    connectionHandler, 
+    resultDatabaseSettings,
+    targetId, 
+    comparatorId, 
+    outcomeId,
+    analysisId, 
+    databaseId = NULL
+) {
+  if(is.null(targetId)){
+    return(NULL)
+  }
+  sql <- "
+    SELECT
+      database_id, target_id, comparator_id, outcome_id, analysis_id, equipoise
+    FROM
+      @schema.@cm_table_prefixdiagnostics_summary cmpsd
+    WHERE
+      cmpsd.target_id = @target_id
+      AND cmpsd.comparator_id = @comparator_id
+      AND cmpsd.analysis_id = @analysis_id 
+      AND cmpsd.outcome_id = @outcome_id 
+  "
+  if(!is.null(databaseId)) {
+    sql <- paste(sql, paste("AND cmpsd.database_id = '@database_id'"), collapse = "\n")
+  }
+  
+  
+  result <- connectionHandler$queryDb(
+    sql = sql,
+    schema = resultDatabaseSettings$schema,
+    cm_table_prefix = resultDatabaseSettings$cmTablePrefix,
+    target_id = targetId,
+    comparator_id = comparatorId,
+    outcome_id = outcomeId,
+    analysis_id = analysisId,
+    database_id = databaseId
+  )
+  
+  
+  if (!is.null(databaseId)) {
+    result$databaseId <- NULL
+  }
+  
+  eq <- round(result$equipoise, 4)
+  
+  return(eq)
+}
+
 getCohortMethodPs <- function(
     connectionHandler, 
     resultDatabaseSettings,
@@ -157,7 +217,7 @@ getCohortMethodPs <- function(
 }
 
 # CohortMethod-propensityScoreDist
-plotCohortMethodPs <- function(ps, targetName, comparatorName) {
+plotCohortMethodPs <- function(ps, targetName, comparatorName, equipoiseStatistic) {
   if(is.null(ps$preferenceScore)){
     return(NULL)
   }
@@ -180,13 +240,17 @@ plotCohortMethodPs <- function(ps, targetName, comparatorName) {
                                            grDevices::rgb(0, 0, 0.8, alpha = 0.5))) +
     ggplot2::scale_x_continuous("Preference score", limits = c(0, 1)) +
     ggplot2::scale_y_continuous("Density") +
+    ggplot2::ggtitle(paste0("Equipoise Statistic = ", equipoiseStatistic)) +
     ggplot2::theme(legend.title = ggplot2::element_blank(),
                    panel.grid.major = ggplot2::element_blank(),
                    panel.grid.minor = ggplot2::element_blank(),
                    legend.position = "top",
                    legend.text = theme,
                    axis.text = theme,
-                   axis.title = theme)
+                   axis.title = theme,
+                   plot.title = ggplot2::element_text(vjust = -20)
+                   ) + 
+    ggplot2::guides(fill = ggplot2::guide_legend(nrow = 2))
   if (!is.null(ps$databaseId)) {
     plot <- plot + ggplot2::facet_grid(databaseId~., switch = "both") +
       ggplot2::theme(legend.position = "right")
diff --git a/R/estimation-cohort-method-systematicError.R b/R/estimation-cohort-method-systematicError.R
index fa1ce54..b2fd49a 100644
--- a/R/estimation-cohort-method-systematicError.R
+++ b/R/estimation-cohort-method-systematicError.R
@@ -33,7 +33,8 @@ cohortMethodSystematicErrorViewer <- function(id) {
     shiny::div(shiny::strong("Figure 4."),"Systematic error. Effect size estimates for the negative controls (true hazard ratio = 1)
                                                                                     and positive controls (true hazard ratio > 1), before and after calibration. Estimates below the diagonal dashed
                                                                                     lines are statistically significant (alpha = 0.05) different from the true effect size. A well-calibrated
-                                                                                    estimator should have the true effect size within the 95 percent confidence interval 95 percent of times."),
+                                                                                    estimator should have the true effect size within the 95 percent confidence interval 95 percent of times. 
+                                                                                    The expected absolute systematic error (EASE) statistic is also shown at the top of the figure."),
     shiny::div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                shiny::downloadButton(outputId = ns("downloadSystematicErrorPlotPng"),
                                      label = "Download plot as PNG"),
@@ -82,6 +83,15 @@ cohortMethodSystematicErrorServer <- function(
             databaseId = row$databaseId
             )
           
+          ease <- estimationGetEase(
+            connectionHandler = connectionHandler,
+            resultDatabaseSettings = resultDatabaseSettings,
+            targetId = row$targetId,
+            comparatorId = row$comparatorId,
+            analysisId = row$analysisId,
+            databaseId = row$databaseId
+          )
+          
           # remove the RR zeros that replace NAs during data upload 
           controlResults$logRr[controlResults$logRr == 0] <- NA
           controlResults$ci95Lb[controlResults$ci95Lb == 0] <- NA
@@ -90,7 +100,7 @@ cohortMethodSystematicErrorServer <- function(
           controlResults$calibratedCi95Lb[controlResults$calibratedCi95Lb == 0] <- NA
           controlResults$calibratedCi95Ub[controlResults$calibratedCi95Ub == 0] <- NA
           
-          plot <- plotCohortMethodScatter(controlResults)
+          plot <- plotCohortMethodScatter(controlResults, ease)
           return(plot)
         }
       })
@@ -99,8 +109,25 @@ cohortMethodSystematicErrorServer <- function(
         return(systematicErrorPlot())
       })
       
+      picName <- shiny::reactive({
+        row <- selectedRow()
+        if (is.null(row)) {
+          return(NULL)
+        } else {
+          picName <- paste0("Target=", stringr::str_trunc(row$target, 35), "_", 
+                            "Comparator=",stringr::str_trunc(row$comparator, 35), "_",
+                            "Analysis=",row$description, "_",
+                            "DB=",row$cdmSourceAbbreviation, "_",
+                 Sys.Date())
+        }
+          
+          return(picName)
+          
+        })
+      
       output$downloadSystematicErrorPlotPng <- shiny::downloadHandler(
-        filename = "SystematicError.png",
+            
+        filename = paste0("SystematicErrorPlot_", picName(), ".png"),
         contentType = "image/png",
         content = function(file) {
           ggplot2::ggsave(file, plot = systematicErrorPlot(), width = 12, height = 5.5, dpi = 400)
@@ -108,7 +135,7 @@ cohortMethodSystematicErrorServer <- function(
       )
       
       output$downloadSystematicErrorPlotPdf <- shiny::downloadHandler(
-        filename = "SystematicError.pdf",
+        filename = paste0("SystematicErrorPlot_", picName(), ".pdf"),
         contentType = "application/pdf",
         content = function(file) {
           ggplot2::ggsave(file = file, plot = systematicErrorPlot(), width = 12, height = 5.5)
@@ -175,7 +202,7 @@ getCohortMethodControlResults <- function(
 }
 
 
-plotCohortMethodScatter <- function(controlResults) {
+plotCohortMethodScatter <- function(controlResults, ease = NULL) {
   
   if(nrow(controlResults)==0){
     return(NULL)
@@ -262,6 +289,7 @@ plotCohortMethodScatter <- function(controlResults) {
                                 labels = breaks) +
     ggplot2::scale_y_continuous("Standard Error", limits = c(0, 1)) +
     ggplot2::facet_grid(yGroup ~ Group) +
+    ggplot2::ggtitle(paste0("EASE Statistic = ", ease)) +
     ggplot2::theme(panel.grid.minor = ggplot2::element_blank(),
                    panel.background = ggplot2::element_blank(),
                    panel.grid.major = ggplot2::element_blank(),
@@ -272,10 +300,60 @@ plotCohortMethodScatter <- function(controlResults) {
                    legend.key = ggplot2::element_blank(),
                    strip.text.x = theme,
                    strip.text.y = theme,
-                   strip.background = ggplot2::element_blank(),
-                   legend.position = "none")
+                   strip.background = ggplot2::element_blank()
+                   # ,
+                   # legend.position = "top",
+                   # legend.text = paste0("EASE = ", ease)
+                   )
   
   return(plot)
 }
 
+estimationGetEase <- function(
+    connectionHandler = connectionHandler,
+    resultDatabaseSettings = resultDatabaseSettings,
+    targetId =  targetId,
+    comparatorId = comparatorId,
+    analysisId = analysisId,
+    databaseId = databaseId
+){
+  
+  sql <- "
+    SELECT DISTINCT
+      dmd.cdm_source_abbreviation database_name,
+      cmds.analysis_id,
+      cmds.target_id,
+      cmds.comparator_id,
+      cmds.max_sdm,
+      cmds.ease
+    FROM
+      @schema.@cm_table_prefixdiagnostics_summary cmds
+      INNER JOIN @schema.@database_table dmd ON dmd.database_id = cmds.database_id
+      
+      where cmds.target_id = @target_id
+      and cmds.comparator_id = @comparator_id
+      and cmds.analysis_id = @analysis_id
+      and cmds.database_id = '@database_id'
+      ;
+  "
+  
+  result <- connectionHandler$queryDb(
+    sql = sql,
+    schema = resultDatabaseSettings$schema,
+    cm_table_prefix = resultDatabaseSettings$cmTablePrefix,
+    database_table = resultDatabaseSettings$databaseTable,
+    target_id = targetId,
+    comparator_id = comparatorId,
+    analysis_id = analysisId,
+    database_id = databaseId
+  )
+  
+  ease <- round(result$ease, 4)
+  
+  return(
+    ease
+  )
+  
+}
+
 
diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf
new file mode 100644
index 0000000..0caff5e
Binary files /dev/null and b/tests/testthat/Rplots.pdf differ
diff --git a/tests/testthat/test-estimation-cohort-method-power.R b/tests/testthat/test-estimation-cohort-method-power.R
index a891e12..9c89c75 100644
--- a/tests/testthat/test-estimation-cohort-method-power.R
+++ b/tests/testthat/test-estimation-cohort-method-power.R
@@ -49,10 +49,10 @@ shiny::testServer(
         comparatorDays = 1000
       )
     )
-    testthat::expect_true(!is.null(output$powerTable))
+    testthat::expect_true(!is.null(powerTable))
     testthat::expect_true(!is.null(output$powerTableCaption))
     testthat::expect_true(!is.null(output$timeAtRiskTableCaption))
-    testthat::expect_true(!is.null(output$timeAtRiskTable))
+    testthat::expect_true(!is.null(timeAtRiskTable))
     
     
   })